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Tabla de Contenidos: “…-- Navigating the Platform -- Introducing Jupyter Notebooks -- Jupyter Features -- Exploring some of Jupyter's most useful features -- Converting a Jupyter Notebook to a Python Script -- Python Libraries -- Import the external libraries and set up the plotting environment -- Our First Analysis - The Boston Housing Dataset -- Loading the Data into Jupyter Using a Pandas DataFrame -- Load the Boston housing dataset -- Data Exploration -- Explore the Boston housing dataset -- Introduction to Predictive Analytics with Jupyter Notebooks -- Linear models with Seaborn and scikit-learn -- Activity: Building a Third-Order Polynomial Model -- Linear models with Seaborn and scikit-learn -- Using Categorical Features for Segmentation Analysis -- Create categorical fields from continuous variables and make segmented visualizations -- Summary -- Chapter 2: Data Cleaning and Advanced Machine Learning -- Preparing to Train a Predictive Model -- Determining a Plan for Predictive Analytics -- Preprocessing Data for Machine Learning -- Exploring data preprocessing tools and methods -- Activity: Preparing to Train a Predictive Model for the Employee-Retention Problem -- Training Classification Models -- Introduction to Classification Algorithms -- Training two-feature classification models with scikit-learn -- The plot_decision_regions Function -- Training k-nearest neighbors for our model -- Training a Random Forest -- Assessing Models with k-Fold Cross-Validation and Validation Curves -- Using k-fold cross-validation and validation curves in Python with scikit-learn -- Dimensionality Reduction Techniques…”

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